Cryptix TX AI

1. Abstract

Cryptix TX AI is a trainable analysis layer designed to identify suspicious wallet and miner behavior. The objective is early detection, structured review, and network protection while keeping normal users outside the analysis path.

The public release is intentionally limited and is used for community-assisted screening and additional data collection. Internal systems run with deeper signal coverage and expanded review tooling.

2. Scope and operating position

• The system estimates risk signals and outputs a suspicion score; it does not assign guilt.
• Manual review remains mandatory before operational actions are taken.
• No centralized user profile is required for participation in the Cryptix network.
• Detection is targeted to suspicious behavior patterns, not broad user surveillance.

3. Public release v1.1.0 - change log

• Fixed a training issue that blocked processing for certain wallet patterns.
• Improved the startup training dataset for more stable first-pass behavior.
• Smoother model adjustment flow with less abrupt value jumps.
• Added a sensitive mode for stricter anomaly treatment.
• Customized sklearn.cluster behavior for project-specific signals.
• Higher precision in value evaluation during training runs.
• Sensitive mode can trigger retraining when a suspicious wallet or miner is flagged.

4. Detection pipeline (simplified)

# simplified review flow (illustrative)
score = model.predict_suspicion(wallet_features)

if score >= threshold_sensitive:
    trigger_retraining(mode="sensitive")
    queue_manual_review(wallet_id, score)
elif score >= threshold_watch:
    add_to_overwatch_list(wallet_id, duration="time-limited")
else:
    mark_clean(wallet_id)

5. Public build vs internal build

The public version is a lightweight release with constrained feature depth. Community tests may mark wallets as suspicious, but this does not automatically match internal system outcomes.

• Public release: limited signal set and training depth for accessible testing.
• Internal operations: expanded variable space (20+ decision signals) and continuous retraining.
• Internal tools include deeper cross-checking and tighter analyst workflows.

6. Mining behavior analysis rationale

TX AI was originally focused on transaction fraud detection and was later extended to mining analysis after observed unfair hardware behavior in the network. Detection logic uses multi-point signatures, including stability patterns, startup behavior, and longitudinal deviations.

The model is trained on both suspicious and normal patterns. This enables boundary learning between regular activity and anomaly clusters. Signature spoofing risk is considered and is part of ongoing model hardening.

7. Governance and privacy boundary

• TX AI provides suspicion scoring, not judicial attribution.
• Cases above threshold are reviewed manually before action.
• Users below operational thresholds are not escalated.
• No email-based account profile is required for network participation.

8. Downloads and live endpoints

The public package is intended for testing and community-assisted detection workflows. Internal developer editions are not publicly distributed at this stage.

9. Limits and ongoing work

• Threshold tuning remains an active process as behavior distributions evolve.
• High-hashrate newcomer profiles may temporarily increase model sensitivity.
• Operational precision depends on retraining cadence and review quality.
• Future work includes stronger automation around alerting and analyst handoff.

Note: this page documents a security-research toolchain. Outputs are advisory signals and must be validated through human review before enforcement.